Gated Spectral State Space Image Encoding for Long Sequences
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Solution Overview
Problem
Transformer and Mamba frameworks face challenges with quadratic computational complexity, increased learning parameters, and training instability, particularly when handling long input sequences, leading to high latency and performance gaps compared to state-of-the-art models.
Innovation Solution
The introduction of a gated spectral state space model (GSSSM) that performs spectral transformations of embedded input image patches, eliminating the need for an initial convolutional neural network layer and using parametrized Gaussian functions to approximate complex matrix-based computations, thereby reducing training parameters and latency while maintaining sub-quadratic computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transformer models are used for processing long sequences, then comprehensive feature representation is achieved, but computational complexity increases quadratically
Solution Approach 1:
The patent transforms the state space model parameters into the spectral domain using Fourier or cosine transforms. This parameter transformation allows the model to process sequences with reduced computational complexity while maintaining the ability to capture long-range dependencies, directly resolving the contradiction between comprehensive feature representation and quadratic computational complexity
Solution Approach 2:
The patent replaces the traditional time-domain state space model computations with frequency-domain operations. By substituting sequential matrix multiplications with spectral transforms and element-wise operations, the model achieves linear or near-linear complexity while preserving representational power
2Productivity
If Mamba framework is used to reduce computational complexity, then processing efficiency improves, but training stability deteriorates
Solution Approach 1:
The patent introduces spectral transforms as an intermediary layer between the input and the state space model computations. This spectral domain acts as a mediator that stabilizes the training process by transforming the optimization landscape, making it more amenable to gradient-based methods while maintaining the efficiency benefits of the Mamba framework
Solution Approach 2:
The patent moves the computations from the time domain to the frequency domain, effectively changing the dimension in which operations are performed. This dimensional transformation stabilizes training by decoupling different frequency components, allowing each to be processed independently with more stable gradient updates
3Manufacturing precision
If initial convolutional neural network layer is used for image processing, then local feature extraction is improved, but overall model latency increases
Solution Approach 1:
The patent extracts and removes the initial convolutional neural network layer from the traditional vision transformer architecture. By taking out this layer, the model directly processes patch embeddings through the spectral state space model, eliminating the latency introduced by convolutional operations while the spectral transforms compensate for local feature extraction capabilities
Solution Approach 2:
The spectral state space model serves multiple functions simultaneously: it performs both the local feature extraction traditionally done by convolutions and the global sequence modeling. This multi-functionality eliminates the need for separate convolutional layers, reducing latency while maintaining feature extraction quality
Data Source
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AI summary
A system may generate embedded subsets by projecting each subset of the subsets into a vector space to generate a corresponding embedded subset. A system may encode the embedded subsets into an encoded image using a dataset encoder including a gated spectral state space model, the gated spectral state space model being a gated neural network that includes a spectral state space model, the spectral state space model being a state space model that represents features of the input dataset using at least a spectral transformation of each embedded subset of the embedded subsets. A system may predict a classification for the input dataset using the encoded image.